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TacGlove
Abstract

We present a design of an easy-to-replicate glove-based system that can reliably perform simultaneous hand pose and force sensing in real time, for the purpose of collecting human hand data during fine manipulative actions. The design consists of a sensory glove that is capable of jointly collecting data of finger poses, hand poses, as well as forces on palm and each phalanx. Specifically, the sensory glove employs a network of 15 IMUs to measure the rotations between individual phalanxes. Hand pose is then reconstructed using forward kinematics. Contact forces on the palm and each phalanx are measured by 6 customized force sensors made from Velostat, a piezoresistive material whose force-voltage relation is investigated. We further develop an open-source software pipeline consisting of drivers and processing code and a system for visualizing hand actions that is compatible with the popular Raspberry Pi architecture. In our experiment, we conduct a series of evaluations that quantitatively characterize both individual sensors and the overall system, proving the effectiveness of the proposed design.

BibTeX

Please cite our paper if you use our code or data.

					
@inproceedings{liu2017glove,
    title={A Glove-based System for Studying Hand-Object Manipulation via Joint Pose and Force Sensing},
    author={Liu, Hangxin and Xie, Xu and Millar, Matt and Edmonds, Mark and Gao, Feng and Zhu, Yixin and Santos, Veronica J and Rothrock, Brandon and Zhu, Song-Chun},
    booktitle={International Conference on Intelligent Robots and Systems (IROS)},
    pages={6617--6624},
    year={2017}
}
					
				

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Device Prototype 3rd Generation
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Acknowledgements

We thank Yaofang Zhang of the UCLA Electrical Engineering Department, Eric Peltola, Alireza Fathaliyan, and Xiaoyu Wang of Biomechatronics Lab at UCLA Mechanical and Aerospace Engineering Department for useful discussions and assistance with experiments. We also thank Carl Turner for useful discussions and assistance with the PCB design. The work reported herein was supported by DARPA XAI grant N66001-17-2-4029, DARPA SIMPLEX grant N66001-15-C-4035 and ONR MURI grant N00014-16-1-2007 (to Zhu), and ONR grant N00014-16-1-2468 (to Santos).